The document provides an introduction to data mining concepts and techniques. It discusses the motivation for data mining due to vast amounts of stored data. It defines data mining as the extraction of interesting and potentially useful patterns from large databases. The document also outlines the key steps in a knowledge discovery process, including data cleaning, transformation, mining, and evaluation. It surveys the major applications and functionalities of data mining, as well as issues that require further research.
The document discusses data mining applications and benefits in e-commerce. It describes common data mining applications like financial data analysis, retail industry analysis, telecommunications analysis, and intrusion detection. It then outlines benefits of data mining in e-commerce such as customer profiling, personalization of service, basket analysis, sales forecasting, and market segmentation.
CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Pr...IJCSEIT Journal
Association rule mining (ARM) is the process of generating rules based on the correlation between the set
of items that the customers purchase.Of late, data mining researchers have improved upon the quality of
association rule mining for business development by incorporating factors like value (utility), quantity of
items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead
to a probable loss of profitable rules.
The advantage of wealth of the customers’ needs information and rules aids the retailer in designing his
store layout[9]. An algorithm CSHURI, Customer Segmentation using HURI, is proposed, a modified
version of HURI [6], finds customers who purchase high profitable rare items and accordingly classify the
customers based on some criteria; for example, a retail business may need to identify valuable customers
who are major contributors to a company’s overall profit. For a potential customer arriving in the store,
which customer group one should belong to according to customer needs, what are the preferred functional
features or products that the customer focuses on and what kind of offers will satisfy the customer, etc.,
finds the key in targeting customers to improve sales [9], which forms the base for customer utility mining.
This document proposes using data mining and clustering methods to determine sales strategy at the Gramedia bookstore in Palembang, Indonesia. Specifically, it aims to analyze transaction data from 2011-2013 to understand best-selling book categories and periods. This would help the company make informed decisions around inventory, pricing, and promotions. The document provides background on the business problem, outlines the research objectives and scope, and reviews relevant literature on data mining, sales strategy, and clustering methods.
The document discusses data mining applications in various domains including biomedical, financial, retail, and telecommunications. It describes how data mining can be used to analyze DNA sequences and biomedical data, detect financial crimes and predict loan payments, analyze customer shopping behaviors in retail, and identify fraudulent patterns in telecommunications data. The document also covers trends in data mining such as visual data mining and audio data mining.
Data mining involves discovering patterns from large data sources and has evolved from database technology. It includes data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Mining can occur on different data sources and involves characterizing, associating, classifying, clustering, and identifying outliers and trends in data. Major issues include scalability, noise handling, pattern evaluation, and privacy concerns.
This document provides an overview of data warehousing and data mining. It defines a data warehouse as a centralized repository of integrated data from various sources used to support management decision making. Key characteristics of a data warehouse include being subject-oriented, integrated, non-volatile, and time-variant. The document contrasts operational data with data in a warehouse and discusses components of a data warehouse system like data acquisition, staging areas, and data marts. It also outlines the history and growth of data warehousing and data mining as well as their applications in domains like marketing, finance, fraud detection, and more.
This document provides an introduction to data mining concepts and techniques. It discusses why data mining is needed due to the abundance of data available. It defines data mining as the extraction of interesting and non-trivial patterns from large datasets. The document outlines the key steps in the knowledge discovery process including data cleaning, transformation, mining, and evaluation. It also describes different types of data that can be mined, such as databases, data warehouses, text, images, and streams. Finally, it covers common data mining functionalities including classification, clustering, association rule mining and prediction.
Data mining final year project in ludhianadeepikakaler1
Are you so occupied with your family and work that you don’t even have any more time left for your MBA assignments or thesis?
E2matrix offer our assistance, writing and consulting services with your research assignments particularly in the areas of thesis, dissertations, journals, online forum discussions, FYP, and so on.
We also provide training for the different technologies and are involved in a wide diversity of subject areas ranging from management,engineering up to programming and designs; and our team of research experts and professional consultants are readily available to help you towards your successful completion of your assignments.
Engage us today at our e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
and can visit our web site-www.e2matrix.com
contact us-7508509709
07508509730
09041262727
Address us - Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara
The document discusses data mining applications and benefits in e-commerce. It describes common data mining applications like financial data analysis, retail industry analysis, telecommunications analysis, and intrusion detection. It then outlines benefits of data mining in e-commerce such as customer profiling, personalization of service, basket analysis, sales forecasting, and market segmentation.
CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Pr...IJCSEIT Journal
Association rule mining (ARM) is the process of generating rules based on the correlation between the set
of items that the customers purchase.Of late, data mining researchers have improved upon the quality of
association rule mining for business development by incorporating factors like value (utility), quantity of
items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead
to a probable loss of profitable rules.
The advantage of wealth of the customers’ needs information and rules aids the retailer in designing his
store layout[9]. An algorithm CSHURI, Customer Segmentation using HURI, is proposed, a modified
version of HURI [6], finds customers who purchase high profitable rare items and accordingly classify the
customers based on some criteria; for example, a retail business may need to identify valuable customers
who are major contributors to a company’s overall profit. For a potential customer arriving in the store,
which customer group one should belong to according to customer needs, what are the preferred functional
features or products that the customer focuses on and what kind of offers will satisfy the customer, etc.,
finds the key in targeting customers to improve sales [9], which forms the base for customer utility mining.
This document proposes using data mining and clustering methods to determine sales strategy at the Gramedia bookstore in Palembang, Indonesia. Specifically, it aims to analyze transaction data from 2011-2013 to understand best-selling book categories and periods. This would help the company make informed decisions around inventory, pricing, and promotions. The document provides background on the business problem, outlines the research objectives and scope, and reviews relevant literature on data mining, sales strategy, and clustering methods.
The document discusses data mining applications in various domains including biomedical, financial, retail, and telecommunications. It describes how data mining can be used to analyze DNA sequences and biomedical data, detect financial crimes and predict loan payments, analyze customer shopping behaviors in retail, and identify fraudulent patterns in telecommunications data. The document also covers trends in data mining such as visual data mining and audio data mining.
Data mining involves discovering patterns from large data sources and has evolved from database technology. It includes data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Mining can occur on different data sources and involves characterizing, associating, classifying, clustering, and identifying outliers and trends in data. Major issues include scalability, noise handling, pattern evaluation, and privacy concerns.
This document provides an overview of data warehousing and data mining. It defines a data warehouse as a centralized repository of integrated data from various sources used to support management decision making. Key characteristics of a data warehouse include being subject-oriented, integrated, non-volatile, and time-variant. The document contrasts operational data with data in a warehouse and discusses components of a data warehouse system like data acquisition, staging areas, and data marts. It also outlines the history and growth of data warehousing and data mining as well as their applications in domains like marketing, finance, fraud detection, and more.
This document provides an introduction to data mining concepts and techniques. It discusses why data mining is needed due to the abundance of data available. It defines data mining as the extraction of interesting and non-trivial patterns from large datasets. The document outlines the key steps in the knowledge discovery process including data cleaning, transformation, mining, and evaluation. It also describes different types of data that can be mined, such as databases, data warehouses, text, images, and streams. Finally, it covers common data mining functionalities including classification, clustering, association rule mining and prediction.
Data mining final year project in ludhianadeepikakaler1
Are you so occupied with your family and work that you don’t even have any more time left for your MBA assignments or thesis?
E2matrix offer our assistance, writing and consulting services with your research assignments particularly in the areas of thesis, dissertations, journals, online forum discussions, FYP, and so on.
We also provide training for the different technologies and are involved in a wide diversity of subject areas ranging from management,engineering up to programming and designs; and our team of research experts and professional consultants are readily available to help you towards your successful completion of your assignments.
Engage us today at our e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
and can visit our web site-www.e2matrix.com
contact us-7508509709
07508509730
09041262727
Address us - Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara
Data mining final year project in jalandhardeepikakaler1
This document provides an introduction to data mining. It defines data mining as the process of extracting interesting and useful patterns from large amounts of data. The document outlines some common applications of data mining such as market analysis, risk analysis, and fraud detection. It also describes the typical steps involved in a data mining process including data cleaning, pattern evaluation, and knowledge presentation. Finally, the document discusses different data mining functionalities like classification, association rule mining, and clustering.
6 weeks summer training in data mining,jalandhardeepikakaler1
e2matrix is a leading Web Design and Development Company now in the field of Industrial training. We provide you 6 Month/6 Week Industrial training in PhP,Web Designing, Java, Dot Net, android Applications.
we also provide work for various technoligies with additional facilities-
RESEARCH PAPERS
OBJECTIVES
SYNOPSIS
IMPLEMENTATION
DOCUMENTATION
REPORT WRITING
PAPER PUBLICATION
Address-Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara,punjab
email addres-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
WEBSITE-www.e2matrix.com
CONTACT NUMBER --
09041262727
07508509730
7508509709
6 weeks summer training in data mining,ludhianadeepikakaler1
E2marix is leading Training & Certification Company offering Corporate Training Programs, IT Education Courses in diversified areas.Since its inception, E2matrix educational Services have trained and certified many students and professionals.
TECHNOLOGIES PROVIDED -
MATLAB
NS2
IMAGE PROCESSING
.NET
SOFTWARE TESTING
DATA MINING
NEURAL networks
HFSS
WEKA
ANDROID
CLOUD computing
COMPUTER NETWORKS
FUZZY LOGIC
ARTIFICIAL INTELLIGENCE
LABVIEW
EMBEDDED
VLSI
Address
Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara
email-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
Web site-www.e2matrix.com
CONTACT NUMBER --
07508509730
09041262727
7508509709
6months industrial training in data mining,ludhianadeepikakaler1
This document provides an introduction to data mining. It discusses the motivation for data mining due to vast amounts of stored data. Data mining aims to extract useful patterns and knowledge from large databases. It can be used for applications like market analysis, risk analysis, and fraud detection. The document outlines the key steps in a typical data mining process, including data selection, cleaning, mining algorithms, and pattern evaluation. It also discusses different types of data mining functionalities, such as classification, association, and clustering. Not all patterns discovered may be interesting, and the document discusses measures for evaluating pattern interestingness.
6months industrial training in data mining, jalandhardeepikakaler1
e2matrix is a leading Web Design and Development Company now in the field of Industrial training. We provide you 6 Month/6 Week Industrial training in PhP,Web Designing, Java, Dot Net, android Applications.
we also provide work for various technoligies with additional facilities-
RESEARCH PAPERS
OBJECTIVES
SYNOPSIS
IMPLEMENTATION
DOCUMENTATION
REPORT WRITING
PAPER PUBLICATION
Address-Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara,punjab
email addres-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
WEBSITE-www.e2matrix.com
CONTACT NUMBER --
09041262727
07508509730
7508509709
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom in a hierarchy. It then discusses why data mining is needed due to the explosive growth of data from various sources. Data mining is defined as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. The knowledge discovery process involves identifying a problem, mining data to transform it into actionable information, acting on the information, and measuring the results. The document outlines different types of data that can be mined, including structured, transactional, time-series, spatial, multimedia, and web data. Common data mining tasks are also described such as classification, prediction, clustering,
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the hierarchy from data to wisdom. It then discusses the growth of data from terabytes to petabytes and major sources of data. Key points made include that while data is growing exponentially, most data is not analyzed due to skills shortage. The document defines data mining as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. It outlines the knowledge discovery process and types of knowledge discovery. Finally, it provides examples of data mining applications.
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom in a hierarchy. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large data repositories. The key aspects of data mining discussed are that it aims to discover previously unknown, implicit and potentially useful patterns from large data sets in an automated manner. The document outlines the interdisciplinary nature of data mining and its relationship to knowledge discovery in databases. It describes the types of data that can be mined, including structured, transactional, time-series and web data, as well as common data mining tasks like classification, prediction and clustering.
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the hierarchy from data to wisdom. It then discusses the growth of data from terabytes to petabytes and major sources of data. Key points made include that while data is growing exponentially, most data is not analyzed due to skills shortage. The document defines data mining as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. It outlines the knowledge discovery process and types of knowledge discovery. Finally, it provides examples of data mining applications.
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large data repositories. Data mining involves non-trivial analysis of implicit patterns in large data sets. It is an interdisciplinary field that draws from areas like machine learning, statistics, database technology, and visualization. The goal is to transform data into actionable information through an iterative process of identifying problems, mining data, acting on results, and measuring impact.
The document discusses data mining and knowledge discovery from large datasets. It begins by defining the terms data, information, knowledge, and wisdom. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large datasets. Data mining involves automated analysis techniques from fields like machine learning, statistics, and database management to discover patterns and relationships in data. The knowledge discovery process involves data preparation, data mining, and evaluation of the extracted patterns. The document provides examples of data mining applications in business, science, fraud detection, and web mining.
The document discusses data mining and knowledge discovery from large datasets. It begins by defining the hierarchy from data to wisdom. It then discusses the growth of big data from various sources and the need for data mining to extract useful knowledge. Data mining involves applying machine learning, statistics, visualization and database techniques to discover patterns in large datasets. The knowledge discovery process involves data cleaning, transformation, data mining and evaluating/interpreting patterns. The document provides examples of data mining applications in business, fraud detection, text mining and web mining.
This document provides an overview of data mining, including its definition, origins, necessity, and applications. Data mining is defined as the extraction of implicit, unknown patterns from large data sets by automatic or semi-automatic means. It has its roots in statistics, artificial intelligence, and machine learning. With huge amounts of data now being collected, data mining is necessary to help organizations discover useful knowledge from their data and gain business insights. It has wide applications in areas like marketing, finance, fraud detection, and health care.
Data mining 1 - Introduction (cheat sheet - printable)yesheeka
This document provides an overview of data mining. It discusses why companies perform data mining, including exploiting profitable real-world uses and addressing the "data explosion" problem. The document also outlines the basic process of knowledge discovery in databases (KDD), including data selection, cleaning, transformation, mining algorithms, and presenting/using the discovered knowledge. Several potential applications of data mining are described, such as market analysis, fraud detection, and other domains like astronomy, sports, and the internet.
The document discusses various applications of data mining, including financial data analysis, retail industry analysis, telecommunications analysis, and biological data analysis. It provides examples of how data mining is used for tasks like customer segmentation, marketing campaign analysis, fraud detection, and gene sequence analysis. The document also covers trends in data mining, such as visual data mining and audio data mining.
This document provides an overview of data mining concepts and techniques. It defines data mining as the extraction of interesting and useful patterns from large amounts of data. The document outlines several potential applications of data mining, including market analysis, risk analysis, and fraud detection. It also describes the typical steps involved in a data mining process, including data cleaning, pattern evaluation, and knowledge presentation. Finally, it discusses different data mining functionalities, such as classification, clustering, and association rule mining.
This document provides an introduction to data mining concepts and techniques. It discusses why data mining is needed due to the massive growth of data. Data mining involves extracting useful patterns from large datasets through techniques such as classification, clustering, association rule mining. It is an interdisciplinary field that draws from areas like machine learning, statistics, database systems and visualization. The document outlines key steps in the knowledge discovery process and issues in data mining like pattern evaluation and scalability.
The document provides an overview of data mining concepts and techniques. It discusses what data mining is, the data mining process, different types of data mining techniques including characterization, association, classification, clustering and outlier analysis. It also covers major issues in data mining such as methodology, performance, handling different data types, and applications.
Data mining involves discovering interesting patterns from large amounts of data. It is an outgrowth of database technology that has wide applications. The data mining process includes data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. Data mining can operate on various data sources and provides techniques for characterization, classification, clustering, association analysis and other functions to discover useful knowledge from data.
This document contains an introduction to a course on data mining techniques. It provides an overview of course administration including class times and assessment components. It also lists some key references and resources for the course, including data mining software and textbooks. The course will cover data mining concepts and applications over 12 weeks through lectures and hands-on exercises. Student assessment will include quizzes, assignments, a midterm exam and final exam.
This document discusses mining complex types of data in data mining, including multidimensional analysis of complex objects, mining spatial, multimedia, time-series, text, and web data. It covers generalizing different types of complex data, such as sets, lists, spatial points, images, and objects. Methods discussed include mining spatial databases through spatial data warehousing and cubes, mining sequences through generalization and pattern extraction, and mining associations in spatial data through progressive refinement.
The document discusses data preprocessing techniques for data mining. It covers why preprocessing is important to ensure quality data and mining results. The major tasks covered are data cleaning, integration, transformation, reduction, and discretization. Data cleaning involves techniques for handling missing data, noisy data, and inconsistencies. Data integration combines multiple data sources. Data transformation includes normalization, aggregation, and feature construction. Data reduction strategies aim to reduce data size for mining while maintaining analytical quality and include cube aggregation, dimensionality reduction, and numerosity reduction.
Data mining final year project in jalandhardeepikakaler1
This document provides an introduction to data mining. It defines data mining as the process of extracting interesting and useful patterns from large amounts of data. The document outlines some common applications of data mining such as market analysis, risk analysis, and fraud detection. It also describes the typical steps involved in a data mining process including data cleaning, pattern evaluation, and knowledge presentation. Finally, the document discusses different data mining functionalities like classification, association rule mining, and clustering.
6 weeks summer training in data mining,jalandhardeepikakaler1
e2matrix is a leading Web Design and Development Company now in the field of Industrial training. We provide you 6 Month/6 Week Industrial training in PhP,Web Designing, Java, Dot Net, android Applications.
we also provide work for various technoligies with additional facilities-
RESEARCH PAPERS
OBJECTIVES
SYNOPSIS
IMPLEMENTATION
DOCUMENTATION
REPORT WRITING
PAPER PUBLICATION
Address-Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara,punjab
email addres-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
WEBSITE-www.e2matrix.com
CONTACT NUMBER --
09041262727
07508509730
7508509709
6 weeks summer training in data mining,ludhianadeepikakaler1
E2marix is leading Training & Certification Company offering Corporate Training Programs, IT Education Courses in diversified areas.Since its inception, E2matrix educational Services have trained and certified many students and professionals.
TECHNOLOGIES PROVIDED -
MATLAB
NS2
IMAGE PROCESSING
.NET
SOFTWARE TESTING
DATA MINING
NEURAL networks
HFSS
WEKA
ANDROID
CLOUD computing
COMPUTER NETWORKS
FUZZY LOGIC
ARTIFICIAL INTELLIGENCE
LABVIEW
EMBEDDED
VLSI
Address
Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara
email-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
Web site-www.e2matrix.com
CONTACT NUMBER --
07508509730
09041262727
7508509709
6months industrial training in data mining,ludhianadeepikakaler1
This document provides an introduction to data mining. It discusses the motivation for data mining due to vast amounts of stored data. Data mining aims to extract useful patterns and knowledge from large databases. It can be used for applications like market analysis, risk analysis, and fraud detection. The document outlines the key steps in a typical data mining process, including data selection, cleaning, mining algorithms, and pattern evaluation. It also discusses different types of data mining functionalities, such as classification, association, and clustering. Not all patterns discovered may be interesting, and the document discusses measures for evaluating pattern interestingness.
6months industrial training in data mining, jalandhardeepikakaler1
e2matrix is a leading Web Design and Development Company now in the field of Industrial training. We provide you 6 Month/6 Week Industrial training in PhP,Web Designing, Java, Dot Net, android Applications.
we also provide work for various technoligies with additional facilities-
RESEARCH PAPERS
OBJECTIVES
SYNOPSIS
IMPLEMENTATION
DOCUMENTATION
REPORT WRITING
PAPER PUBLICATION
Address-Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara,punjab
email addres-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
WEBSITE-www.e2matrix.com
CONTACT NUMBER --
09041262727
07508509730
7508509709
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom in a hierarchy. It then discusses why data mining is needed due to the explosive growth of data from various sources. Data mining is defined as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. The knowledge discovery process involves identifying a problem, mining data to transform it into actionable information, acting on the information, and measuring the results. The document outlines different types of data that can be mined, including structured, transactional, time-series, spatial, multimedia, and web data. Common data mining tasks are also described such as classification, prediction, clustering,
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the hierarchy from data to wisdom. It then discusses the growth of data from terabytes to petabytes and major sources of data. Key points made include that while data is growing exponentially, most data is not analyzed due to skills shortage. The document defines data mining as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. It outlines the knowledge discovery process and types of knowledge discovery. Finally, it provides examples of data mining applications.
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom in a hierarchy. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large data repositories. The key aspects of data mining discussed are that it aims to discover previously unknown, implicit and potentially useful patterns from large data sets in an automated manner. The document outlines the interdisciplinary nature of data mining and its relationship to knowledge discovery in databases. It describes the types of data that can be mined, including structured, transactional, time-series and web data, as well as common data mining tasks like classification, prediction and clustering.
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the hierarchy from data to wisdom. It then discusses the growth of data from terabytes to petabytes and major sources of data. Key points made include that while data is growing exponentially, most data is not analyzed due to skills shortage. The document defines data mining as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. It outlines the knowledge discovery process and types of knowledge discovery. Finally, it provides examples of data mining applications.
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large data repositories. Data mining involves non-trivial analysis of implicit patterns in large data sets. It is an interdisciplinary field that draws from areas like machine learning, statistics, database technology, and visualization. The goal is to transform data into actionable information through an iterative process of identifying problems, mining data, acting on results, and measuring impact.
The document discusses data mining and knowledge discovery from large datasets. It begins by defining the terms data, information, knowledge, and wisdom. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large datasets. Data mining involves automated analysis techniques from fields like machine learning, statistics, and database management to discover patterns and relationships in data. The knowledge discovery process involves data preparation, data mining, and evaluation of the extracted patterns. The document provides examples of data mining applications in business, science, fraud detection, and web mining.
The document discusses data mining and knowledge discovery from large datasets. It begins by defining the hierarchy from data to wisdom. It then discusses the growth of big data from various sources and the need for data mining to extract useful knowledge. Data mining involves applying machine learning, statistics, visualization and database techniques to discover patterns in large datasets. The knowledge discovery process involves data cleaning, transformation, data mining and evaluating/interpreting patterns. The document provides examples of data mining applications in business, fraud detection, text mining and web mining.
This document provides an overview of data mining, including its definition, origins, necessity, and applications. Data mining is defined as the extraction of implicit, unknown patterns from large data sets by automatic or semi-automatic means. It has its roots in statistics, artificial intelligence, and machine learning. With huge amounts of data now being collected, data mining is necessary to help organizations discover useful knowledge from their data and gain business insights. It has wide applications in areas like marketing, finance, fraud detection, and health care.
Data mining 1 - Introduction (cheat sheet - printable)yesheeka
This document provides an overview of data mining. It discusses why companies perform data mining, including exploiting profitable real-world uses and addressing the "data explosion" problem. The document also outlines the basic process of knowledge discovery in databases (KDD), including data selection, cleaning, transformation, mining algorithms, and presenting/using the discovered knowledge. Several potential applications of data mining are described, such as market analysis, fraud detection, and other domains like astronomy, sports, and the internet.
The document discusses various applications of data mining, including financial data analysis, retail industry analysis, telecommunications analysis, and biological data analysis. It provides examples of how data mining is used for tasks like customer segmentation, marketing campaign analysis, fraud detection, and gene sequence analysis. The document also covers trends in data mining, such as visual data mining and audio data mining.
This document provides an overview of data mining concepts and techniques. It defines data mining as the extraction of interesting and useful patterns from large amounts of data. The document outlines several potential applications of data mining, including market analysis, risk analysis, and fraud detection. It also describes the typical steps involved in a data mining process, including data cleaning, pattern evaluation, and knowledge presentation. Finally, it discusses different data mining functionalities, such as classification, clustering, and association rule mining.
This document provides an introduction to data mining concepts and techniques. It discusses why data mining is needed due to the massive growth of data. Data mining involves extracting useful patterns from large datasets through techniques such as classification, clustering, association rule mining. It is an interdisciplinary field that draws from areas like machine learning, statistics, database systems and visualization. The document outlines key steps in the knowledge discovery process and issues in data mining like pattern evaluation and scalability.
The document provides an overview of data mining concepts and techniques. It discusses what data mining is, the data mining process, different types of data mining techniques including characterization, association, classification, clustering and outlier analysis. It also covers major issues in data mining such as methodology, performance, handling different data types, and applications.
Data mining involves discovering interesting patterns from large amounts of data. It is an outgrowth of database technology that has wide applications. The data mining process includes data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. Data mining can operate on various data sources and provides techniques for characterization, classification, clustering, association analysis and other functions to discover useful knowledge from data.
This document contains an introduction to a course on data mining techniques. It provides an overview of course administration including class times and assessment components. It also lists some key references and resources for the course, including data mining software and textbooks. The course will cover data mining concepts and applications over 12 weeks through lectures and hands-on exercises. Student assessment will include quizzes, assignments, a midterm exam and final exam.
This document discusses mining complex types of data in data mining, including multidimensional analysis of complex objects, mining spatial, multimedia, time-series, text, and web data. It covers generalizing different types of complex data, such as sets, lists, spatial points, images, and objects. Methods discussed include mining spatial databases through spatial data warehousing and cubes, mining sequences through generalization and pattern extraction, and mining associations in spatial data through progressive refinement.
The document discusses data preprocessing techniques for data mining. It covers why preprocessing is important to ensure quality data and mining results. The major tasks covered are data cleaning, integration, transformation, reduction, and discretization. Data cleaning involves techniques for handling missing data, noisy data, and inconsistencies. Data integration combines multiple data sources. Data transformation includes normalization, aggregation, and feature construction. Data reduction strategies aim to reduce data size for mining while maintaining analytical quality and include cube aggregation, dimensionality reduction, and numerosity reduction.
This chapter discusses data warehousing and OLAP technology for data mining. It defines what a data warehouse is, including that it is a decision support database maintained separately from operational databases that contains consolidated, historical data. It also describes multi-dimensional data models using data cubes and dimensional hierarchies. Common data warehouse architectures like star schemas and snowflake schemas are presented. Finally, it discusses how OLAP operations on these multi-dimensional models support data mining.
The document summarizes key concepts from Chapter 8 of the textbook "Data Mining: Concepts and Techniques" which covers cluster analysis. It discusses different types of data that can be used for cluster analysis as well as major clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based approaches. Specific partitioning algorithms covered are k-means and k-medoids clustering.
This document summarizes Chapter 7 of the textbook "Data Mining: Concepts and Techniques" which covers the topics of classification and prediction in data mining. The chapter discusses classification using decision tree induction, Bayesian classification, backpropagation, association rule mining, and other methods. It also addresses evaluating classification methods, handling issues like data preparation and overfitting, and measuring classification accuracy. The goal of classification is to predict categorical class labels, while prediction models continuous values.
This document provides an overview of chapter 6 from the textbook "Data Mining: Concepts and Techniques" which discusses mining association rules from large databases. The chapter covers association rule mining, the Apriori algorithm for finding frequent itemsets, methods to improve Apriori's efficiency such as hashing and partitioning, and the FP-growth method for mining frequent patterns without candidate generation by compressing a database into a frequent-pattern tree.
This document summarizes Chapter 5 of the textbook "Data Mining: Concepts and Techniques". It discusses concept description, which involves characterizing data through generalization, summarization, and comparison of different classes. Key aspects covered include data cube approaches to characterization, attribute-oriented induction for generalization, analytical characterization of attribute relevance, and presenting generalized results through cross-tabulation, visualization, and rules. Implementation can utilize pre-computed data cubes to enable efficient analysis operations like drill-down.
This document discusses data mining primitives, languages, and system architectures. It describes five primitives that define a data mining task: task-relevant data, type of knowledge to be mined, background knowledge, pattern interestingness measurements, and visualization of discovered patterns. It also discusses data mining query languages like DMQL that allow users to specify data mining tasks interactively. Finally, it covers different architectures for coupling data mining systems with database/data warehouse systems.
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2. January 20, 2018 2
Chapter 1. Introduction
Motivation: Why data mining?
What is data mining?
Data Mining: On what kind of data?
Data mining functionality
Are all the patterns interesting?
Classification of data mining systems
Major issues in data mining
3. January 20, 2018 3
Motivation: “Necessity is the
Mother of Invention”
Data explosion problem
Automated data collection tools and mature database
technology lead to tremendous amounts of data stored in
databases, data warehouses and other information repositories
We are drowning in data, but starving for knowledge!
Solution: Data warehousing and data mining
Data warehousing and on-line analytical processing
Extraction of interesting knowledge (rules, regularities, patterns,
constraints) from data in large databases
4. January 20, 2018 4
Evolution of Database Technology
(See Fig. 1.1)
1960s:
Data collection, database creation, IMS and network DBMS
1970s:
Relational data model, relational DBMS implementation
1980s:
RDBMS, advanced data models (extended-relational, OO,
deductive, etc.) and application-oriented DBMS (spatial,
scientific, engineering, etc.)
1990s—2000s:
Data mining and data warehousing, multimedia databases, and
Web databases
5. January 20, 2018 5
What Is Data Mining?
Data mining (knowledge discovery in databases):
Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) information or patterns
from data in large databases
Alternative names and their “inside stories”:
Data mining: a misnomer?
Knowledge discovery(mining) in databases (KDD),
knowledge extraction, data/pattern analysis, data
archeology, data dredging, information harvesting,
business intelligence, etc.
What is not data mining?
(Deductive) query processing.
Expert systems or small ML/statistical programs
6. January 20, 2018 6
Why Data Mining? — Potential
Applications
Database analysis and decision support
Market analysis and management
target marketing, customer relation management, market
basket analysis, cross selling, market segmentation
Risk analysis and management
Forecasting, customer retention, improved underwriting,
quality control, competitive analysis
Fraud detection and management
Other Applications
Text mining (news group, email, documents) and Web analysis.
Intelligent query answering
7. January 20, 2018 7
Market Analysis and Management (1)
Where are the data sources for analysis?
Credit card transactions, loyalty cards, discount coupons,
customer complaint calls, plus (public) lifestyle studies
Target marketing
Find clusters of “model” customers who share the same
characteristics: interest, income level, spending habits, etc.
Determine customer purchasing patterns over time
Conversion of single to a joint bank account: marriage, etc.
Cross-market analysis
Associations/co-relations between product sales
Prediction based on the association information
8. January 20, 2018 8
Market Analysis and Management (2)
Customer profiling
data mining can tell you what types of customers buy what
products (clustering or classification)
Identifying customer requirements
identifying the best products for different customers
use prediction to find what factors will attract new customers
Provides summary information
various multidimensional summary reports
statistical summary information (data central tendency and
variation)
9. January 20, 2018 9
Corporate Analysis and Risk
Management
Finance planning and asset evaluation
cash flow analysis and prediction
contingent claim analysis to evaluate assets
cross-sectional and time series analysis (financial-ratio, trend
analysis, etc.)
Resource planning:
summarize and compare the resources and spending
Competition:
monitor competitors and market directions
group customers into classes and a class-based pricing
procedure
set pricing strategy in a highly competitive market
10. January 20, 2018 10
Fraud Detection and Management (1)
Applications
widely used in health care, retail, credit card services,
telecommunications (phone card fraud), etc.
Approach
use historical data to build models of fraudulent behavior and
use data mining to help identify similar instances
Examples
auto insurance: detect a group of people who stage accidents to
collect on insurance
money laundering: detect suspicious money transactions (US
Treasury's Financial Crimes Enforcement Network)
medical insurance: detect professional patients and ring of
doctors and ring of references
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Fraud Detection and Management (2)
Detecting inappropriate medical treatment
Australian Health Insurance Commission identifies that in many
cases blanket screening tests were requested (save Australian
$1m/yr).
Detecting telephone fraud
Telephone call model: destination of the call, duration, time of
day or week. Analyze patterns that deviate from an expected
norm.
British Telecom identified discrete groups of callers with frequent
intra-group calls, especially mobile phones, and broke a
multimillion dollar fraud.
Retail
Analysts estimate that 38% of retail shrink is due to dishonest
employees.
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Other Applications
Sports
IBM Advanced Scout analyzed NBA game statistics (shots
blocked, assists, and fouls) to gain competitive advantage for
New York Knicks and Miami Heat
Astronomy
JPL and the Palomar Observatory discovered 22 quasars with
the help of data mining
Internet Web Surf-Aid
IBM Surf-Aid applies data mining algorithms to Web access
logs for market-related pages to discover customer preference
and behavior pages, analyzing effectiveness of Web marketing,
improving Web site organization, etc.
13. January 20, 2018 13
Data Mining: A KDD Process
Data mining: the core of
knowledge discovery
process.
Data Cleaning
Data Integration
Databases
Data
Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation
14. January 20, 2018 14
Steps of a KDD Process
Learning the application domain:
relevant prior knowledge and goals of application
Creating a target data set: data selection
Data cleaning and preprocessing: (may take 60% of effort!)
Data reduction and transformation:
Find useful features, dimensionality/variable reduction, invariant
representation.
Choosing functions of data mining
summarization, classification, regression, association, clustering.
Choosing the mining algorithm(s)
Data mining: search for patterns of interest
Pattern evaluation and knowledge presentation
visualization, transformation, removing redundant patterns, etc.
Use of discovered knowledge
15. January 20, 2018 15
Data Mining and Business
Intelligence
Increasing potential
to support
business decisions End User
Business
Analyst
Data
Analyst
DBA
Making
Decisions
Data Presentation
Visualization Techniques
Data Mining
Information Discovery
Data Exploration
OLAP, MDA
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
Data Sources
Paper, Files, Information Providers, Database Systems, OLTP
16. January 20, 2018 16
Architecture of a Typical Data
Mining System
Data
Warehouse
Data cleaning & data integration Filtering
Databases
Database or data
warehouse server
Data mining engine
Pattern evaluation
Graphical user interface
Knowledge-base
17. January 20, 2018 17
Data Mining: On What Kind of
Data?
Relational databases
Data warehouses
Transactional databases
Advanced DB and information repositories
Object-oriented and object-relational databases
Spatial databases
Time-series data and temporal data
Text databases and multimedia databases
Heterogeneous and legacy databases
WWW
18. January 20, 2018 18
Data Mining Functionalities (1)
Concept description: Characterization and
discrimination
Generalize, summarize, and contrast data
characteristics, e.g., dry vs. wet regions
Association (correlation and causality)
Multi-dimensional vs. single-dimensional association
age(X, “20..29”) ^ income(X, “20..29K”) buys(X,
“PC”) [support = 2%, confidence = 60%]
contains(T, “computer”) contains(x, “software”) [1%,
75%]
19. January 20, 2018 19
Data Mining Functionalities (2)
Classification and Prediction
Finding models (functions) that describe and distinguish classes
or concepts for future prediction
E.g., classify countries based on climate, or classify cars based
on gas mileage
Presentation: decision-tree, classification rule, neural network
Prediction: Predict some unknown or missing numerical values
Cluster analysis
Class label is unknown: Group data to form new classes, e.g.,
cluster houses to find distribution patterns
Clustering based on the principle: maximizing the intra-class
similarity and minimizing the interclass similarity
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Data Mining Functionalities (3)
Outlier analysis
Outlier: a data object that does not comply with the general behavior of
the data
It can be considered as noise or exception but is quite useful in fraud
detection, rare events analysis
Trend and evolution analysis
Trend and deviation: regression analysis
Sequential pattern mining, periodicity analysis
Similarity-based analysis
Other pattern-directed or statistical analyses
21. January 20, 2018 21
Are All the “Discovered” Patterns
Interesting?
A data mining system/query may generate thousands of patterns,
not all of them are interesting.
Suggested approach: Human-centered, query-based, focused mining
Interestingness measures: A pattern is interesting if it is easily
understood by humans, valid on new or test data with some degree
of certainty, potentially useful, novel, or validates some hypothesis
that a user seeks to confirm
Objective vs. subjective interestingness measures:
Objective: based on statistics and structures of patterns, e.g., support,
confidence, etc.
Subjective: based on user’s belief in the data, e.g., unexpectedness,
novelty, actionability, etc.
22. January 20, 2018 22
Can We Find All and Only
Interesting Patterns?
Find all the interesting patterns: Completeness
Can a data mining system find all the interesting patterns?
Association vs. classification vs. clustering
Search for only interesting patterns: Optimization
Can a data mining system find only the interesting patterns?
Approaches
First general all the patterns and then filter out the
uninteresting ones.
Generate only the interesting patterns—mining query
optimization
23. January 20, 2018 23
Data Mining: Confluence of Multiple
Disciplines
Data Mining
Database
Technology
Statistics
Other
Disciplines
Information
Science
Machine
Learning
Visualization
24. January 20, 2018 24
Data Mining: Classification
Schemes
General functionality
Descriptive data mining
Predictive data mining
Different views, different classifications
Kinds of databases to be mined
Kinds of knowledge to be discovered
Kinds of techniques utilized
Kinds of applications adapted
25. January 20, 2018 25
A Multi-Dimensional View of Data
Mining Classification
Databases to be mined
Relational, transactional, object-oriented, object-relational,
active, spatial, time-series, text, multi-media, heterogeneous,
legacy, WWW, etc.
Knowledge to be mined
Characterization, discrimination, association, classification,
clustering, trend, deviation and outlier analysis, etc.
Multiple/integrated functions and mining at multiple levels
Techniques utilized
Database-oriented, data warehouse (OLAP), machine learning,
statistics, visualization, neural network, etc.
Applications adapted
Retail, telecommunication, banking, fraud analysis, DNA mining, stock
market analysis, Web mining, Weblog analysis, etc.
26. January 20, 2018 26
OLAP Mining: An Integration of Data
Mining and Data Warehousing
Data mining systems, DBMS, Data warehouse
systems coupling
No coupling, loose-coupling, semi-tight-coupling, tight-coupling
On-line analytical mining data
integration of mining and OLAP technologies
Interactive mining multi-level knowledge
Necessity of mining knowledge and patterns at different levels of
abstraction by drilling/rolling, pivoting, slicing/dicing, etc.
Integration of multiple mining functions
Characterized classification, first clustering and then association
27. January 20, 2018 27
Major Issues in Data Mining (1)
Mining methodology and user interaction
Mining different kinds of knowledge in databases
Interactive mining of knowledge at multiple levels of abstraction
Incorporation of background knowledge
Data mining query languages and ad-hoc data mining
Expression and visualization of data mining results
Handling noise and incomplete data
Pattern evaluation: the interestingness problem
Performance and scalability
Efficiency and scalability of data mining algorithms
Parallel, distributed and incremental mining methods
28. January 20, 2018 28
Major Issues in Data Mining (2)
Issues relating to the diversity of data types
Handling relational and complex types of data
Mining information from heterogeneous databases and global
information systems (WWW)
Issues related to applications and social impacts
Application of discovered knowledge
Domain-specific data mining tools
Intelligent query answering
Process control and decision making
Integration of the discovered knowledge with existing knowledge:
A knowledge fusion problem
Protection of data security, integrity, and privacy
29. January 20, 2018 29
Summary
Data mining: discovering interesting patterns from large amounts of
data
A natural evolution of database technology, in great demand, with
wide applications
A KDD process includes data cleaning, data integration, data
selection, transformation, data mining, pattern evaluation, and
knowledge presentation
Mining can be performed in a variety of information repositories
Data mining functionalities: characterization, discrimination,
association, classification, clustering, outlier and trend analysis, etc.
Classification of data mining systems
Major issues in data mining
30. January 20, 2018 30
A Brief History of Data Mining
Society
1989 IJCAI Workshop on Knowledge Discovery in Databases
(Piatetsky-Shapiro)
Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)
1991-1994 Workshops on Knowledge Discovery in Databases
Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-
Shapiro, P. Smyth, and R. Uthurusamy, 1996)
1995-1998 International Conferences on Knowledge Discovery in
Databases and Data Mining (KDD’95-98)
Journal of Data Mining and Knowledge Discovery (1997)
1998 ACM SIGKDD, SIGKDD’1999-2001 conferences, and SIGKDD
Explorations
More conferences on data mining
PAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc.
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Where to Find References?
Data mining and KDD (SIGKDD member CDROM):
Conference proceedings: KDD, and others, such as PKDD, PAKDD, etc.
Journal: Data Mining and Knowledge Discovery
Database field (SIGMOD member CD ROM):
Conference proceedings: ACM-SIGMOD, ACM-PODS, VLDB, ICDE,
EDBT, DASFAA
Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIIS, etc.
AI and Machine Learning:
Conference proceedings: Machine learning, AAAI, IJCAI, etc.
Journals: Machine Learning, Artificial Intelligence, etc.
Statistics:
Conference proceedings: Joint Stat. Meeting, etc.
Journals: Annals of statistics, etc.
Visualization:
Conference proceedings: CHI, etc.
Journals: IEEE Trans. visualization and computer graphics, etc.
32. January 20, 2018 32
References
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in
Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan
Kaufmann, 2000.
T. Imielinski and H. Mannila. A database perspective on knowledge discovery.
Communications of ACM, 39:58-64, 1996.
G. Piatetsky-Shapiro, U. Fayyad, and P. Smith. From data mining to knowledge
discovery: An overview. In U.M. Fayyad, et al. (eds.), Advances in Knowledge
Discovery and Data Mining, 1-35. AAAI/MIT Press, 1996.
G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases.
AAAI/MIT Press, 1991.